Murnawan

Work place: Department of Information System, Widyatama University, Indonesia

E-mail: murnawan@widyatama.ac.id

Website:

Research Interests: Machine Learning, Business Intelligence, Data Mining

Biography

Murnawan, M.T., is a lecturer in the Department of Information Systems at Widyatama University in Bandung, Indonesia. He received a master's degree in informatics with a focus on information system studies from the Institut Teknologi Bandung (ITB), Bandung, Indonesia. His area of interest includes Business Intelligence, Data Warehouse, Data Mining, Decision Support Systems, and Machine Learning. For communication.

Author Articles
Enhancing Mobile Software Developer Selection through Integrated F-AHP and F-TOPSIS Methods

By Murnawan Vaya Viora Novitasari

DOI: https://doi.org/10.5815/ijieeb.2024.04.01, Pub. Date: 8 Aug. 2024

This study delves into the impact of employee recruiting within the dynamic and fiercely competitive realm of information technology (IT), focusing on the role of mobile software developers in a software development company situated in Bandung, Indonesia. Given that the quality of employees and their alignment with organizational needs are pivotal drivers of productivity and overall performance, the recruitment process assumes paramount importance. However, this process is riddled with complexity and challenges, stemming from the need to define precise criteria and navigate decision-making amidst uncertainty and ambiguity. To confront these challenges, this research advocates for the utilization of the Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS). The F-AHP method, employing Chang's extent analysis approach, assists in establishing weights for uncertain criteria. Meanwhile, F-TOPSIS is leveraged to evaluate alternatives based on predefined criteria. The focal point of this study is the selection of mobile software developers within a software development company in Bandung, Indonesia. Decision-makers, drawing insights from policy documents and assessment forms, identified pertinent criteria and sub-criteria. Utilizing F-AHP, they determined the weights for criteria and sub-criteria through paired comparisons using fuzzy numbers. Subsequently, F-TOPSIS was applied to rank 10 mobile software developer candidates, culminating in the identification of alternative-7 (CK-7) as the top mobile software developer candidate. In essence, the application of F-AHP and F-TOPSIS methods presents an effective approach to navigate the complexity of Multi-Criteria Decision Making (MCDM) in employee selection, particularly within the competitive landscape of the information technology industry. This study's findings underscore the significance of employing advanced decision-making techniques to enhance the efficiency and effectiveness of employee recruitment processes, thereby bolstering organizational performance and competitiveness.

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